class_docstr: An implementation for an explainable neural network for natural
  language processing and generating rationales used by that network.
  Based on the paper "Rethinking Cooperative Rationalization: Introspective
  Extraction and Complement Control" by Yu et. al.
code: def _train_one_step(self, X_tokens, label, baseline, mask):
      self.opt_E_anti.zero_grad()
      self.opt_E.zero_grad()
      self.opt_G_sup.zero_grad()
      self.opt_G_rl.zero_grad()
  
      forward_dict = self.forward(X_tokens, mask)
      predict = forward_dict["predict"]
      anti_predict = forward_dict["anti_predict"]
      cls_predict = forward_dict["cls_predict"]
      z = forward_dict["z"]
      neg_log_probs = forward_dict["neg_log_probs"]
  
      e_loss_anti = torch.mean(self.loss_func(anti_predict, label))
      # cls_pred dimensions: (batch_size,)
      _, cls_pred = torch.max(cls_predict, dim=1)
      e_loss = (
          torch.mean(self.loss_func(predict, label))
          + torch.mean(self.loss_func(predict, cls_pred))
      ) / 2
  
      # g_sup_loss comes from only cls pred loss
      (
          g_sup_loss,
          g_rl_loss,
          rewards,
          consistency_loss,
          continuity_loss,
          sparsity_loss,
      ) = self._get_loss(
          predict,
          anti_predict,
          cls_predict,
          label,
          z,
          neg_log_probs,
          baseline,
          mask,
      )
  
      losses = {
          "e_loss": e_loss.cpu().data,
          "e_loss_anti": e_loss_anti.cpu().data,
          "g_sup_loss": g_sup_loss.cpu().data,
          "g_rl_loss": g_rl_loss.cpu().data,
          "consistency_loss": consistency_loss,
          "continuity_loss": continuity_loss,
          "sparsity_loss": sparsity_loss,
      }
  
      e_loss_anti.backward(retain_graph=True)
      self.opt_E_anti.step()
      self.opt_E_anti.zero_grad()
  
      e_loss.backward(retain_graph=True)
      self.opt_E.step()
      self.opt_E.zero_grad()
  
      if not self.fixed_classifier:
          g_sup_loss.backward(retain_graph=True)
          self.opt_G_sup.step()
          self.opt_G_sup.zero_grad()
  
      g_rl_loss.backward(retain_graph=True)
      self.opt_G_rl.step()
      self.opt_G_rl.zero_grad()
  
      return (losses, predict, anti_predict, cls_predict, z, rewards)
  
imports: import logging
  import os
  import random
  from collections import deque
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from interpret_text.experimental.common.base_text_model import BaseTextModel
  from torch.autograd import Variable
  from tqdm import tqdm
  from interpret_text.experimental.common.utils_introspective_rationale import generate_data
label_desc: Use image node_id 6 for calling the IntrospectiveRationaleModel obj's underlying member method code with example usage: obj._train_one_step(X_tokens, label, baseline, mask) and returns: losses,  predict,  anti_predict,  cls_predict,  z,  rewards